Luofeng Liao

Me at a graffiti laneway in Melbourne

Hi! My name is Luofeng Liao (廖烙锋 in Chinese) and I am a first-year phd student in the IEOR Department at Columbia.

My research interests are deep/machine learning methods for causal inference and econometrics, reinforcement learning with observational data, nonparametric statistics and adversarial/minimax optimization.

Prior to Columbia, I received my bachelor’s degree in computer science in Fudan University in China and a master’s degree in statistics in University of Chicago. In UChicago, I used to work with Prof. Mladen Kolar and Prof. Zhaoran Wang.

Papers

preprint

Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems

Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, and Dacheng Tao

2021

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Large scale convex-concave minimax problems arise in numerous applications, including game theory, robust training, and training of generative adversarial networks. Despite their wide applicability, solving such problems efficiently and effectively is challenging in the presence of large amounts of data using existing stochastic minimax methods. We study a class of stochastic minimax methods and develop a communication-efficient distributed stochastic extragradient algorithm, LocalAdaSEG, with an adaptive learning rate suitable for solving convex-concave minimax problem in the Parameter-Server model. LocalAdaSEG has three main features: (i) periodic communication strategy reduces the communication cost between workers and the server; (ii) an adaptive learning rate that is computed locally and allows for tuning-free implementation; and (iii) theoretically, a nearly linear speed-up with respect to the dominant variance term, arising from estimation of the stochastic gradient, is proven in both the smooth and nonsmooth convex-concave settings. LocalAdaSEG is used to solve a stochastic bilinear game, and train generative adversarial network. We compare LocalAdaSEG against several existing optimizers for minimax problems and demonstrate its efficacy through several experiments in both the homogeneous and heterogeneous settings
preprint

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Mladen Kolar, and Zhaoran Wang

2021

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In offline reinforcement learning (RL) an optimal policy is learnt solely from a priori collected observational data. However, in observational data, actions are often confounded by unobserved variables. Instrumental variables (IVs), in the context of RL, are the variables whose influence on the state variables are all mediated through the action. When a valid instrument is present, we can recover the confounded transition dynamics through observational data. We study a confounded Markov decision process where the transition dynamics admit an additive nonlinear functional form. Using IVs, we derive a conditional moment restriction (CMR) through which we can identify transition dynamics based on observational data. We propose a provably efficient IV-aided Value Iteration (IVVI) algorithm based on a primal-dual reformulation of CMR. To the best of our knowledge, this is the first provably efficient algorithm for instrument-aided offline RL.
NeurIPS2020

Provably efficient neural estimation of structural equation model: An adversarial approach

Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, and Mladen Kolar

2020

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Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration and estimate structural parameters of interest. We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation. We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using the stochastic gradient descent. We consider both 2-layer and multi-layer NNs with ReLU activation functions and prove global convergence in an overparametrized regime, where the number of neurons is diverging. The results are established using techniques from online learning and local linearization of NNs, and improve in several aspects the current state-of-the-art. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.